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Table 10 NIS-related DEA studies

From: Efficiency evaluation of BRICS’s national innovation systems based on bias-corrected network data envelopment analysis

Article Method Variables
Inputs Outputs
Matei & Aldea, 2012 DEA
Innovation leaders; Innovation followers; Moderate innovators; Modest innovators
• New doctorate graduates (ISCED 6) per 1000 population.
• International scientific co-publications per million population.
• Public R&D expenditures as % of GDP.
• Business R&D expenditures as % of GDP.
• patents applications per billion GDP.
trademarks per billion GDP
• Trademarks per billion GDP.
• Employment in knowledge-intensive activities (manufacturing and services) as % of total employment.
• Medium and high-tech product exports as % total product exports.
• Knowledge-intensive services exports as % total service exports.
Guan & Chen, 2010 CRS- output oriented Two stages DEA process • R&D expenditure.
• Technology import.
• Patent applications.
• High-tech export.
Lee & Park, 2005 DEA
The output oriented CCR model
Anova—ANOVA and Post-hoc Comparisons
inventors, merchandisers, academicians, and duds
• R&D expenditure.
• Average number of researchers.
• Technology balance of receipts.
• Number of scientific and technical journal articles.
• Number of triadic patent families.
Guan & Chen, 2012 DEA
CRS and VRS, Network (2-stage)-output oriented Super efficiency
Tobit regression on environmental factors
• Number of full-time equivalent scientists and engineers.
• Incremental R&D expenditure funding.
• Innovation activities.
• Prior accumulated knowledge stock breeding upstream knowledge production.
• Consumed full-time equivalent labour for non-R&D activities.
• Number of patents granted.
• Number of patents granted.
• International scientific papers.
• Added value of industries.
• Export of new products in high-tech industries.
Lu et al., 2014 Network DEA • Total R&D personnel.
• Public expenditures on education.
• Import of goods and commercial services.
• Total expenditures on R&D.
• Published scientific articles.
• Patents (residents and nonresidents).
Carayannis et al., 2015 VRS-multistage, multilevel (2 stages
x 2 levels)
• Science graduates in tertiary education.
• Participation in lifelong learning.
• Total R&D expenditure.
• R&D capital stock.
• Citable documents.
• Patent applications.
• Employment in knowledge intensive services/manufacturing.
• SMEs collaborating with others.
• Venture capital investment.
• High Tech Exports.
• Sales of new to market and new to firm innovation.
• License and patent revenues from abroad.
• Number of trademark applications in national offices.
Wang & Huang, 2007 Three-stage approach
Input-oriented DEA – BCC; Tobit regressions; Parameter estimates from the second stage are used to predict the total input slacks.
• Fixed capital formation.
• Researchers.
• Technicians
• Patents.
• SCI Papers.
• EI Papers.
Chen et al., 2011 DEA–output-oriented- CRS • Total R&D manpower.
• R&D expenditure stocks.
• Patents.
• Scientific journal articles.
• Royalty and licensing fees.
Pan et al., 2010 Input- oriented DEA model • Total public expenditure on education.
• Imports of goods and commercial services.
• Total expenditure on R&D.
• Direct investment stocks abroad.
• Total R&D personnel nationwide.
• Number of patents granted to residents.
• Number of patents secured abroad by national residents.
• Scientific articles published by origin of author.
Cai, 2011 DEA + OLS Regression • R&D expenditure as a % of GDP.
• Total R&D personnel.
• Patents per 1000 population.
• Scientific articles per 1000 population.
• High-tech exports as a % of total manufacturing exports.
Afzal, 2014 Output- oriented DEA- CRS + Tobit regression model • Population ages 15 to 65 (% of total) as labour force.
• Computer users per 1000.
• Domestic credit provided by banking sector (% of GDP).
• R&D expenditure % GDP.
• School enrolment, secondary (%gross).
• Cost of business start-up procedure (% of GNI per capita).
• Regulatory quality.
• Openness (Trade (% of GDP).
• Total natural resources rents (% of GDP).
• High-tech export as % total manufacturing exports.
Jon M. Zabala-Iturriagagoitia et al., 2007 DEA • Property right; medium-tech industries.
• Public R&D expenditure R&D.
• Business R&D expenditure.
• The percentage of the population between 25 and 64 years of age with a higher education
• Patents.
• GDP per capita.
Kou et al., 2016 Multi-period and multi-division systems (MPMDS), Dynamic network DEA (DN–DEA) • R&D expenditure.
• R&D personnel.
• S&T papers.
• Technology import.
• Export of high -tech products.
• GDP of employment (The ratio of gross domestic product (GDP) to total employment in the economy).
Nasierowski & Arcelus, 2003 Two step- DEA (CCR) input-orientation + PCA (two principal components analysis) • Imports of goods and commercial services.
• Gross domestic expenditure on research.
• Employment in R&D.
• Total educational expenditures.
• External patents by resident.
• Patents by a country’s residents.
• National productivity.
Furman et al., 2002 Modeling national innovative capacity based on Romer formulation • Patents.
• Patent per million.
• R&D expenditure.
• Openness.
• Education expenditure.
• R&D spending by private sector.
• R&D spending by Universities.
• Publications.
• GDP.
• Capital Stock.
• High-tech exports.
Crespo & Crespo, 2016 Fuzzy-set qualitative comparative analysis. • Institutions.
• Human capital and research.
• Infrastructure.
• Market sophistication.
• Business sophistication.
Filippetti & Peyrache, 2011 DEA and PCA • Triadic patents.
• Business R&D (BERD).
• Total researchers in R&D (FTE).
• Scientific and technical articles.
• Public R&D.
• Higher Education Expenditure on R&D.
• Labour force with tertiary education.
Zhao et al., 2015 Ordinal Multidimensional Scaling and Cluster analysis
Wang, Zhao, & Zhang, 2016 The time lags effects of innovation input on output in the NISs • Researchers in R&D (per million people).
• R&D expenditure (% of GDP).
• Regulatory quality.
• University-industry research collaboration.
• Patent applications, residents.
Sesay et al., 2018 Dynamic Panel Data Analysis
NIS ➔ Economic Growth
• University enrolment rate for science and engineering students.
• government research and development expenditure.
• High-tech export.
• Total number of patents.
• Scientific personnel.
• Scientific and technical journal articles.
• Economic freedom.
Proksch et al., 2017 Fuzzy-set qualitative comparative analysis (fsQCA) • International patents per million inhabitants.
• GDP per capita.
• Stock of international patents.
• Aggregate R&D expenditures.
• Openness.
• Strength of protection for IP.
• Share of government expenditure on higher education.
• Stringency of antitrust policies.
• Specialization degree.
• New business registered.
• Capital formation.
Pires & Garcia, 2012 Stochastic Frontier Analysis (SFA) productivity analysis • GDP growth.
• Capital accumulation.
• Labour expansion.
• Change in GDP per worker.
• R&D expenditures.
• Average years of schooling of population over 25 years.
Ivanova et al., 2017 Economic complexity index; Patent complexity index; Triple-helix complexity index Patent and groups of products.
Altuntas et al., 2016 A fuzzy-logic based data-mining approach to assess innovation capability of manufacturing systems
Samara et al., 2012 The paper analyses the impact of innovation Policies on the NIS performance based on system dynamics (SD) • Public Expenditure on R&D.
• Private Expenditures on R&D.
• Patent.
• Trademark.
• Total public education expenditure.
• Population with tertiary education per 100 population aged.
• Doctorate graduates per 1000 population aged.
• Government debt (% GDP).
• Total tax rate.
• Number of procedures required to start a business.
• Venture capital.
• Employment in knowledge intensive services (% of workforce).